#!/usr/bin/env python
# Copyright (c) 2019, Anthony Latorre <tlatorre at uchicago>
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <https://www.gnu.org/licenses/>.
"""
Script to plot final fit results along with sidebands for the dark matter
analysis. To run it just run:

    $ ./plot-energy [list of fit results]

Currently it will plot energy distributions for external muons, michel
electrons, atmospheric events with neutron followers, and prompt signal like
events. Each of these plots will have a different subplot for the particle ID
of the best fit, i.e. single electron, single muon, double electron, electron +
muon, or double muon.

When run with the --dc command line argument it instead produces corner plots
showing the distribution of the high level variables used in the contamination
analysis for all the different instrumental backgrounds and external muons.
"""
from __future__ import print_function, division
import numpy as np
from scipy.stats import iqr, poisson
from matplotlib.lines import Line2D
from scipy.stats import iqr, norm, beta
from scipy.special import spence
from itertools import izip_longest

particle_id = {20: 'e', 22: r'\mu'}

def plot_hist2(df, muons=False):
    for id, df_id in sorted(df.groupby('id')):
        if id == 20:
            plt.subplot(2,3,1)
        elif id == 22:
            plt.subplot(2,3,2)
        elif id == 2020:
            plt.subplot(2,3,4)
        elif id == 2022:
            plt.subplot(2,3,5)
        elif id == 2222:
            plt.subplot(2,3,6)

        if muons:
            plt.hist(np.log10(df_id.ke.values/1000), bins=np.linspace(0,4.5,100), histtype='step')
            plt.xlabel("log10(Energy (GeV))")
        else:
            bins = np.logspace(np.log10(20),np.log10(10e3),21)
            plt.hist(df_id.ke.values, bins=bins, histtype='step')
            plt.gca().set_xscale("log")
            plt.xlabel("Energy (MeV)")
        plt.title('$' + ''.join([particle_id[int(''.join(x))] for x in grouper(str(id),2)]) + '$')

    if len(df):
        plt.tight_layout()

def plot_hist(df, muons=False):
    for id, df_id in sorted(df.groupby('id')):
        if id == 20:
            plt.subplot(3,4,1)
        elif id == 22:
            plt.subplot(3,4,2)
        elif id == 2020:
            plt.subplot(3,4,5)
        elif id == 2022:
            plt.subplot(3,4,6)
        elif id == 2222:
            plt.subplot(3,4,7)
        elif id == 202020:
            plt.subplot(3,4,9)
        elif id == 202022:
            plt.subplot(3,4,10)
        elif id == 202222:
            plt.subplot(3,4,11)
        elif id == 222222:
            plt.subplot(3,4,12)

        if muons:
            plt.hist(np.log10(df_id.ke.values/1000), bins=np.linspace(0,4.5,100), histtype='step')
            plt.xlabel("log10(Energy (GeV))")
        else:
            plt.hist(df_id.ke.values, bins=np.linspace(20,10e3,100), histtype='step')
            plt.xlabel("Energy (MeV)")
        plt.title(str(id))

    if len(df):
        plt.tight_layout()

if __name__ == '__main__':
    import argparse
    import numpy as np
    import pandas as pd
    import sys
    import h5py
    from sddm.plot_energy import *
    from sddm.plot import despine
    from sddm import setup_matplotlib

    parser = argparse.ArgumentParser("plot fit results")
    parser.add_argument("filenames", nargs='+', help="input files")
    parser.add_argument("--dc", action='store_true', default=False, help="plot corner plots for backgrounds")
    parser.add_argument("--save", action='store_true', default=False, help="save corner plots for backgrounds")
    args = parser.parse_args()

    setup_matplotlib(args.save)

    import matplotlib.pyplot as plt

    ev, fits = get_events(args.filenames)

    if args.dc:
        ev = ev[ev.prompt]

        ev.set_index(['run','gtid'])

        ev = pd.merge(fits,ev,how='inner',on=['run','gtid'])
        ev_single_particle = ev[(ev.id2 == 0) & (ev.id3 == 0)]
        ev_single_particle = ev_single_particle.sort_values('fmin').groupby(['run','gtid']).nth(0)
        ev = ev.sort_values('fmin').groupby(['run','gtid']).nth(0)

        ev['cos_theta'] = np.cos(ev['theta1'])
        ev['udotr'] = np.sin(ev_single_particle.theta1)*np.cos(ev_single_particle.phi1)*ev_single_particle.x + \
                      np.sin(ev_single_particle.theta1)*np.sin(ev_single_particle.phi1)*ev_single_particle.y + \
                      np.cos(ev_single_particle.theta1)*ev_single_particle.z
        ev['udotr'] /= ev.r

        flashers = ev[ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ITC | DC_BREAKDOWN) == DC_FLASHER]
        muon = ev[ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ITC | DC_BREAKDOWN | DC_MUON) == DC_MUON]
        neck = ev[(ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_NECK)) == DC_NECK]
        noise = ev[(ev.dc & (DC_ITC | DC_QVNHIT | DC_JUNK | DC_CRATE_ISOTROPY)) != 0]
        breakdown = ev[ev.nhit >= 1000]
        breakdown = breakdown[breakdown.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_NECK | DC_ITC) == 0]
        breakdown = breakdown[breakdown.dc & (DC_FLASHER | DC_BREAKDOWN) != 0]
        signal = ev[ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ITC | DC_BREAKDOWN | DC_MUON) == 0]

        with pd.option_context('display.max_rows', None, 'display.max_columns', None):
            print("Noise events")
            print(noise[['psi','x','y','z','id1','id2']])
            print("Muons")
            print(muon[['psi','r','id1','id2','id3','energy1','energy2','energy3']])
            print("Neck")
            print(neck[neck.psi < 6][['psi','r','id1','cos_theta']])
            print("Flashers")
            print(flashers[flashers.udotr > 0])
            print("Signal")
            print(signal)

        # save as PDF b/c EPS doesn't support alpha values
        if args.save:
            plot_corner_plot(breakdown,"Breakdowns",save="breakdown_corner_plot")
            plot_corner_plot(muon,"Muons",save="muon_corner_plot")
            plot_corner_plot(flashers,"Flashers",save="flashers_corner_plot")
            plot_corner_plot(neck,"Neck",save="neck_corner_plot")
            plot_corner_plot(noise,"Noise",save="noise_corner_plot")
            plot_corner_plot(signal,"Signal",save="signal_corner_plot")
        else:
            plot_corner_plot(breakdown,"Breakdowns")
            plot_corner_plot(muon,"Muons")
            plot_corner_plot(flashers,"Flashers")
            plot_corner_plot(neck,"Neck")
            plot_corner_plot(noise,"Noise")
            plot_corner_plot(signal,"Signal")

        fig = plt.figure()
        plot_hist2(flashers)
        despine(fig,trim=True)
        plt.suptitle("Flashers")
        fig = plt.figure()
        plot_hist2(muon,muons=True)
        despine(fig,trim=True)
        plt.suptitle("Muons")
        plt.show()
        sys.exit(0)

    # First, do basic data cleaning which is done for all events.
    ev = ev[ev.dc & (DC_JUNK | DC_CRATE_ISOTROPY | DC_QVNHIT | DC_FLASHER | DC_NECK | DC_ITC | DC_BREAKDOWN) == 0]

    # 00-orphan cut
    ev = ev[(ev.gtid & 0xff) != 0]

    print("number of events after data cleaning = %i" % np.count_nonzero(ev.prompt))

    # Now, we select events tagged by the muon tag which should tag only
    # external muons. We keep the sample of muons since it's needed later to
    # identify Michel electrons and to apply the muon follower cut
    muons = ev[(ev.dc & DC_MUON) != 0]

    print("number of muons = %i" % len(muons))

    # Try to identify Michel electrons. Currently, the event selection is based
    # on Richie's thesis. Here, we do the following:
    #
    # 1. Apply more data cleaning cuts to potential Michel electrons
    # 2. Nhit >= 100
    # 3. It must be > 800 ns and less than 20 microseconds from a prompt event
    #    or a muon
    michel = ev.groupby('run',group_keys=False).apply(michel_cut)

    print("number of michel events = %i" % len(michel))

    # Tag atmospheric events.
    #
    # Note: We don't cut atmospheric events or muons yet because we still need
    # all the events in order to apply the muon follower cut.
    ev = ev.groupby('run',group_keys=False).apply(atmospheric_events)

    print("number of events after neutron follower cut = %i" % np.count_nonzero(ev.prompt & (~ev.atm)))

    # remove events 200 microseconds after a muon
    ev = ev.groupby('run',group_keys=False).apply(muon_follower_cut)

    # Get rid of muon events in our main event sample
    ev = ev[(ev.dc & DC_MUON) == 0]

    prompt = ev[ev.prompt & ~ev.atm]

    atm = ev[ev.atm]

    print("number of events after muon cut = %i" % len(prompt))

    # Check to see if there are any events with missing fit information
    atm_ra = atm[['run','gtid']].to_records(index=False)
    muons_ra = muons[['run','gtid']].to_records(index=False)
    prompt_ra = prompt[['run','gtid']].to_records(index=False)
    michel_ra = michel[['run','gtid']].to_records(index=False)
    fits_ra = fits[['run','gtid']].to_records(index=False)

    if len(atm_ra) and np.count_nonzero(~np.isin(atm_ra,fits_ra)):
        print_warning("skipping %i atmospheric events because they are missing fit information!" % np.count_nonzero(~np.isin(atm_ra,fits_ra)))

    if len(muons_ra) and np.count_nonzero(~np.isin(muons_ra,fits_ra)):
        print_warning("skipping %i muon events because they are missing fit information!" % np.count_nonzero(~np.isin(muons_ra,fits_ra)))

    if len(prompt_ra) and np.count_nonzero(~np.isin(prompt_ra,fits_ra)):
        print_warning("skipping %i signal events because they are missing fit information!" % np.count_nonzero(~np.isin(prompt_ra,fits_ra)))

    if len(michel_ra) and np.count_nonzero(~np.isin(michel_ra,fits_ra)):
        print_warning("skipping %i Michel events because they are missing fit information!" % np.count_nonzero(~np.isin(michel_ra,fits_ra)))

    # Now, we merge the event info with the fitter info.
    #
    # Note: This means that the dataframe now contains multiple rows for each
    # event, one for each fit hypothesis.
    atm = pd.merge(fits,atm,how='inner',on=['run','gtid'])
    muons = pd.merge(fits,muons,how='inner',on=['run','gtid'])
    michel = pd.merge(fits,michel,how='inner',on=['run','gtid'])
    prompt = pd.merge(fits,prompt,how='inner',on=['run','gtid'])

    # get rid of events which don't have a fit
    nan = np.isnan(prompt.fmin.values)

    if np.count_nonzero(nan):
        print_warning("skipping %i signal events because the negative log likelihood is nan!" % len(prompt[nan].groupby(['run','gtid'])))

    prompt = prompt[~nan]

    nan_atm = np.isnan(atm.fmin.values)

    if np.count_nonzero(nan_atm):
        print_warning("skipping %i atmospheric events because the negative log likelihood is nan!" % len(atm[nan_atm].groupby(['run','gtid'])))

    atm = atm[~nan_atm]

    nan_muon = np.isnan(muons.fmin.values)

    if np.count_nonzero(nan_muon):
        print_warning("skipping %i muons because the negative log likelihood is nan!" % len(muons[nan_muon].groupby(['run','gtid'])))

    muons = muons[~nan_muon]

    nan_michel = np.isnan(michel.fmin.values)

    if np.count_nonzero(nan_michel):
        print_warning("skipping %i michel electron events because the negative log likelihood is nan!" % len(michel[nan_michel].groupby(['run','gtid'])))

    michel = michel[~nan_michel]

    # get the best fit
    prompt = prompt.sort_values('fmin').groupby(['run','gtid']).nth(0)
    atm = atm.sort_values('fmin').groupby(['run','gtid']).nth(0)
    michel_best_fit = michel.sort_values('fmin').groupby(['run','gtid']).nth(0)
    muon_best_fit = muons.sort_values('fmin').groupby(['run','gtid']).nth(0)
    muons = muons[muons.id == 22].sort_values('fmin').groupby(['run','gtid'],as_index=False).nth(0).reset_index(level=0,drop=True)

    # require (r/r_psup)^3 < 0.9
    prompt = prompt[prompt.r_psup < 0.9]
    atm = atm[atm.r_psup < 0.9]

    print("number of events after radius cut = %i" % len(prompt))

    # require psi < 6
    prompt = prompt[prompt.psi < 6]
    atm = atm[atm.psi < 6]

    print("number of events after psi cut = %i" % len(prompt))

    fig = plt.figure()
    plot_hist2(prompt)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("prompt.pdf")
        plt.savefig("prompt.eps")
    else:
        plt.suptitle("Without Neutron Follower")
    fig = plt.figure()
    plot_hist2(atm)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("atm.pdf")
        plt.savefig("atm.eps")
    else:
        plt.suptitle("With Neutron Follower")
    fig = plt.figure()
    plot_hist2(michel_best_fit)
    despine(fig,trim=True)
    if args.save:
        plt.savefig("michel_electrons.pdf")
        plt.savefig("michel_electrons.eps")
    else:
        plt.suptitle("Michel Electrons")
    fig = plt.figure()
    plot_hist2(muon_best_fit,muons=True)
    despine(fig,trim=True)
    if len(muon_best_fit):
        plt.tight_layout()
    if args.save:
        plt.savefig("external_muons.pdf")
        plt.savefig("external_muons.eps")
    else:
        plt.suptitle("External Muons")

    # Plot the energy and angular distribution for external muons
    fig = plt.figure()
    plt.subplot(2,1,1)
    plt.hist(muons.ke.values, bins=np.logspace(3,7,100), histtype='step')
    plt.xlabel("Energy (MeV)")
    plt.gca().set_xscale("log")
    plt.subplot(2,1,2)
    plt.hist(np.cos(muons.theta.values), bins=np.linspace(-1,1,100), histtype='step')
    despine(fig,trim=True)
    plt.xlabel(r"$\cos(\theta)$")
    plt.tight_layout()
    if args.save:
        plt.savefig("muon_energy_cos_theta.pdf")
        plt.savefig("muon_energy_cos_theta.eps")
    else:
        plt.suptitle("Muons")

    # For the Michel energy plot, we only look at the single particle electron
    # fit
    michel = michel[michel.id == 20].sort_values('fmin').groupby(['run','gtid'],as_index=False).nth(0).reset_index(level=0,drop=True)

    stopping_muons = pd.merge(muons,michel,left_on=['run','gtid'],right_on=['run','muon_gtid'],suffixes=('','_michel'))

    if len(stopping_muons):
        # project muon to PSUP
        stopping_muons['dx'] = stopping_muons.apply(get_dx,axis=1)
        # energy based on distance travelled
        stopping_muons['T_dx'] = dx_to_energy(stopping_muons.dx)
        stopping_muons['dT'] = stopping_muons['energy1'] - stopping_muons['T_dx']

        fig = plt.figure()
        plt.hist((stopping_muons['energy1']-stopping_muons['T_dx'])*100/stopping_muons['T_dx'], bins=np.linspace(-100,100,200), histtype='step')
        despine(fig,trim=True)
        plt.xlabel("Fractional energy difference (\%)")
        plt.title("Fractional energy difference for Stopping Muons")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_fractional_energy_difference.pdf")
            plt.savefig("stopping_muon_fractional_energy_difference.eps")
        else:
            plt.title("Stopping Muon Fractional Energy Difference")

        # 100 bins between 50 MeV and 10 GeV
        bins = np.arange(50,10000,1000)

        pd_bins = pd.cut(stopping_muons['energy1'],bins)

        T = (bins[1:] + bins[:-1])/2

        dT = stopping_muons.groupby(pd_bins)['dT'].agg(['mean','sem','std',std_err,median,median_err,iqr_std,iqr_std_err])

        fig = plt.figure()
        plt.errorbar(T,dT['median']*100/T,yerr=dT['median_err']*100/T)
        despine(fig,trim=True)
        plt.xlabel("Kinetic Energy (MeV)")
        plt.ylabel(r"Energy bias (\%)")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_energy_bias.pdf")
            plt.savefig("stopping_muon_energy_bias.eps")
        else:
            plt.title("Stopping Muon Energy Bias")

        fig = plt.figure()
        plt.errorbar(T,dT['iqr_std']*100/T,yerr=dT['iqr_std_err']*100/T)
        despine(fig,trim=True)
        plt.xlabel("Kinetic Energy (MeV)")
        plt.ylabel(r"Energy resolution (\%)")
        plt.tight_layout()
        if args.save:
            plt.savefig("stopping_muon_energy_resolution.pdf")
            plt.savefig("stopping_muon_energy_resolution.eps")
        else:
            plt.title("Stopping Muon Energy Resolution")

    fig = plt.figure()
    bins=np.linspace(0,100,100)
    plt.hist(michel.ke.values, bins=bins, histtype='step', label="Dark Matter Fitter")
    if michel.size:
        plt.hist(michel[~np.isnan(michel.rsp_energy.values)].rsp_energy.values, bins=np.linspace(20,100,100), histtype='step',label="RSP")
    x = np.linspace(0,100,1000)
    y = michel_spectrum(x)
    y /= np.trapz(y,x=x)
    N = len(michel)
    plt.plot(x, N*y*(bins[1]-bins[0]), ls='--', color='k', label="Michel Spectrum")
    despine(fig,trim=True)
    plt.xlabel("Energy (MeV)")
    plt.tight_layout()
    plt.legend()
    if args.save:
        plt.savefig("michel_electrons_ke.pdf")
        plt.savefig("michel_electrons_ke.eps")
    else:
        plt.title("Michel Electrons")
    plt.show()